{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['凌水', '七贤岭', '龙王塘'])\n",
      "dict_values([[81, 81, 76, 77, 138, 163, 124], [108, 106, 93, 86, 91, 56, 108], [26, 25, 25, 26, 86, 66, 93]])\n"
     ]
    }
   ],
   "source": [
    "base_data = {\n",
    "    \"凌水\":[81, 81, 76, 77, 138, 163, 124],\n",
    "    \"七贤岭\":[108, 106, 93, 86, 91, 56, 108],\n",
    "    \"龙王塘\":[26, 25, 25, 26, 86, 66, 93],\n",
    "}\n",
    "labels = base_data.keys()\n",
    "values = base_data.values()\n",
    "print(labels)\n",
    "print(values)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'凌水': [81, 81, 76, 77, 138, 163, 124],\n",
       " '七贤岭': [108, 106, 93, 86, 91, 56, 108],\n",
       " '龙王塘': [26, 25, 25, 26, 86, 66, 93]}"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "base_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\"\n",
    "imshow即image show，即show image，显示图像的意思\n",
    "\"\"\"\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# 创建数据\n",
    "data = np.random.rand(10, 10)  # 生成一个10x10的随机数组\n",
    "\n",
    "# 创建热图\n",
    "fig, ax = plt.subplots()\n",
    "cax = ax.imshow(data, cmap=\"viridis\")  # 使用viridis颜色映射\n",
    "\n",
    "# 添加颜色条\n",
    "fig.colorbar(cax, ax=ax, label=\"Data value\")\n",
    "\n",
    "# 显示图表\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
